Abstract

RESEARCH DESIGN AND METHODS A case-cohort study within a prospective study was designed. We randomly selected a subcohort (n = 2,500) from 26,444 participants, of whom 1,962 were diabetes free at baseline. Of the 801 incident type 2 diabetes cases identified in the cohort during 7 years of follow-up, 579 remained for analyses after exclusions. Prediction models were compared by receiver operatoring characteristic (ROC) curve and integrated discrimination improvement.

Accurate identification of individuals who are at increased risk for type 2 diabetes is a requirement for a targeted prevention. We therefore tested whether metabolic and genetic markers add substantial prognostic information to age, anthropometry, and lifestyle characteristics.

RESEARCH DESIGN AND METHODS

The European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study involves 27,548 participants (16,644 women, mainly aged 35–65 years, and 10,904 men, mainly aged 40–65 years) recruited from the general population in Potsdam, Germany, between 1994 and 1998. Follow-up questionnaires were sent out every 2–3 years to identify incident cases of type 2 diabetes (response rates 93–97%), and self-reports were verified by questionnaires mailed to physicians. Informed consent was obtained from participants; approval was given by the ethics committee of the State of Brandenburg, Germany.

A prospective case-cohort study was designed (1) (supplemental Fig. 1, which can be found in the online appendix [available at http://care.diabetesjournals.org/cgi/content/full/dc09-0197/DC1]). Of 2,500 individuals randomly selected from 26,444 participants with collected blood, 1,962 remained after exclusion of prevalent diabetes, self-reported but unverified diabetes during follow-up, missing biomarker data, abnormal plasma glucose, or more than four missing genotypes. Of 801 incident cases identified in the full cohort with blood samples (mean follow-up 7.1 years), 579 remained for analyses after exclusions.

We used baseline information on age, waist circumference, height, history of hypertension, physical activity, smoking, and consumption of red meat, whole-grain bread, coffee, and alcohol to compute the German Diabetes Risk Score (DRS), a prediction model previously described (2). Measurement of glucose, HDL cholesterol, triglycerides, γ-glutamyltransferase, alanine aminotransferase, high-sensitivity C-reactive protein (hs-CRP), and A1C followed standard procedures (1). Total adiponectin was measured with an ELISA (LINCO Research, St. Charles, MO). Genotyping of 20 single nucleotide polymorphisms (SNPs) related to diabetes risk (3–6) (supplemental Tables 1–2) was performed with TaqMan technology (Applied Biosystems, Foster City, CA). The genotyping error was ≤0.5%, and genotype distributions were in Hardy-Weinberg equilibrium (P > 0.05). We computed an unweighted count genetic score assuming an additive genetic model for each SNP, applying a linear weighting of 0, 1, and 2 to genotypes containing 0, 1, or 2 risk alleles, respectively. Scores for individuals with missing genotypes were standardized to those for individuals with complete data (7).

We evaluated different prediction models through receiver operatoring characteristics (ROCs) based on logistic regression models comparing the area under the curve (AUC) of the fuller model with that of the sparser model (8). Model calibration was tested by Hosmer-Lemeshow tests (9). Reclassification was evaluated by the integrated discrimination improvement (IDI) (10). Analyses were performed with SAS (version 9.1; SAS Institute, Cary, NC). P values are two-tailed; P < 0.05 was considered statistically significant.

RESULTS

Baseline characteristics of the random subcohort and incident cases are presented in supplemental Table 3. ROC-AUC increased significantly when A1C or glucose was incorporated into a model with the German DRS (Table 1), most notably for A1C (from 0.8464 to 0.8862). P values for Hosmer-Lemeshow tests indicated better model calibration when A1C (P = 0.1181) or glucose (P = 0.3823) was included compared with a model with the German DRS alone (P = 0.0157). Measuring both glucose and A1C improved case-control discrimination. Also, blood lipids, γ-glutamyltransferase, and alanine aminotransferase significantly improved discrimination beyond the German DRS, A1C, and glucose (ROC-AUC: 0.9000). Moreover, IDI, as a marker of improved risk classification, was significantly different from zero (Table 1). In contrast to hs-CRP, additional information on adiponectin improved ROC-AUC. However, relative IDI was rather small (1.5%).

Relative contribution of the German DRS and biochemical and genetic markers to prediction of type 2 diabetes risk

Diabetes risk increased with increasing number of prevalent risk alleles (supplemental Fig. 2). When genetic information was included along with the German DRS and metabolic markers, improvements of ROC-AUC and IDI were small and nonsignificant (Table 1).

CONCLUSIONS

Numerous diabetes prediction models have been developed, but few studies have investigated whether metabolic markers improve prediction beyond conventional risk factors. In the Atherosclerosis Risk in Communities study, clinical variables combined with fasting plasma glucose discriminated better compared with clinical variables only (11). Further improvement in discrimination was observed with HDL cholesterol and triglycerides. In the Framingham Offspring Study, a model involving additional information on hypertension, fasting plasma glucose, HDL cholesterol, and triglycerides performed substantially better than a model including age, sex, BMI, and parental history (12). Our results support and extend these findings by indicating that a comprehensive basic model including important lifestyle risk factors such as physical activity, smoking, alcohol consumption, and diet is significantly improved by glucose, A1C, HDL cholesterol, triglycerides, and liver enzymes but not hs-CRP, adiponectin, or genetic markers.

Our observation that multiple SNPs do not substantially improve discrimination beyond age, sex, and clinical markers confirms previous studies (7,13–15). We extended their results by using a comprehensive set of anthropometric, nutritional, and lifestyle variables in the basic model; by including additional biomarkers (adiponectin, hs-CRP, and A1C); and by using the full set of currently confirmed diabetogenic SNPs. It should be noted that the prospective design rendered it necessary to exclude prevalent diabetes cases at baseline. Thus, results reflect genetic prediction in middle-aged individuals but not prediction at birth. Furthermore, we did not consider gene-gene interactions.

Acknowledgments

The recruitment phase of the EPIC-Potsdam study was supported by the Federal Ministry of Science, Germany (01 EA 9401), and the European Union (SOC 95201408 05F02). The follow-up of the EPIC-Potsdam study was supported by the German Cancer Aid (70-2488-Ha I) and the European Community (SOC 98200769 05F02). The present study was supported by grants from the European Union (EUGENE2: LSHM-CT 204 512013) and the German Ministry of Science and Technology (NGFN2: 01GS0487).

No potential conflicts of interest relevant to this article were reported.

Parts of this study were accepted for presentation in abstract form at the 45th Annual Meeting of the European Association for the Study of Diabetes, Vienna, Austria, 29 September–2 October 2009.

We gratefully acknowledge the excellent technical assistance of Anna Bury, Sabina Herbert, Albrecht Pfäfflin, and Ingo Besenthal, who were involved in the biochemical analyses, and of Frank Döring, who was involved in the genetic analyses. We also thank Kay Behling, Kathrein Kühn, Birgit Czullay, Birgit Schmidtchen, Ellen Kohlsdorf, and Wolfgang Bernigau for their efforts in data collection and management, as well as Janine Kröger for computational assistance.

Footnotes

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